ChromaTOF Tile — The Fast-track to Chemical Insights
ChromaTOF Tile is an extremely effective tool for rapid comparisons of GCxGC data, designed to get labs from data to decisions faster than ever.
Tile efficiently compiles and reviews the raw GCxGC data to highlight differences between the samples or sets of samples. A variety of sample comparison options are available to accommodate different experimental designs and analysis objectives. Visualization and summary views like PCA and loading plots visually identify chemical features and classes, creating captivating publication figures.
Chemical differentiators are clearly defined for the analyst, aiding workflows including, but not limited to: Quality control (i.e. product failure analysis, raw material and final product screening), biomarker discovery, and differentiating sources or treatment groups.
Ready-to-Deliver Results
Fisher Ratio
For data sets with defined groups and replicates, the Fisher Ratio test yields optimal results. Key chemical differentiators between the groups are surfaced, while intra-group variation is suppressed, so you can focus only on what’s important. Great for biomarker discovery, comparing ingredient sources, and more
Coefficient of Variation
For data sets without defined sample groups, the Coefficient of Variation test can help identify underlying chemical signatures that link or differentiate samples. Great first step for isolating meaningful traits from confounding variables, like in complex screening experiments, blinded data sets, and more.
Fold Change
For data sets without replicates, the Fold Change test can expedite chemical insights. Great for instances where rapid results are the priority, like product failure analysis, off-odor identification, and more.
Tile in Action
The Fisher Ratio test uses a chromatographic tiling approach to connect and compare data. Class-to-class variation is compared relative to “within class” variation. This test highlights differences between specific sample classes in data sets where other variation and differences are also expected. It’s best used for experiments where the analysis goals involve comparing different classes or groups of samples with each other.
The Coefficient of Variation test uses a chromatographic tiling approach to connect and compare data. It finds differences by calculating the standard deviation across the sample set relative to the mean. It’s an efficient way to focus data analysis and quickly uncover information about your samples. This test is most effective when reviewing individual analytes and exploring chemical differences, especially when sample groups are unknown or unclear.
The Fold Change test uses a chromatographic tiling approach to connect and compare data. This test can be performed with just two samples, making it perfect for direct comparisons. Raw data is rapidly compared to find the retention windows where there are differences between the samples. The Fold Change test is best used in experiments where you are looking for analytes that may relate to a cause in differentiation of a sample.
Decoding Baijiu's Complex Flavors
Unlock the secrets of China's national liquor—Download our application note to explore how advanced GCxGC-TOFMS analysis reveals the unique aroma profiles of various baijiu spirits.
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Distinctive Flavors of Barrel-Aged Syrups
Unlock the secrets of flavor—Download our insightful application note to discover how bourbon barrel-aging transforms maple syrup through advanced analytical techniques.
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Distinguishing Pesto Flavors with Ease
Unlock the secrets of pesto flavor—Download our application note to explore how advanced analytical techniques reveal the unique aroma profiles of various pesto products.
Analyze the Aroma TodayTheory of Operation
LECO sought strategies to bypass the tedious task of manual GCxGC contour plot comparison, freeing analysts to pursue the next project or rapidly progress key research goals. Traditional peak finding and peak table filtering wouldn’t be fast enough, and couldn’t easily differentiate injection variability from real sample chemistry differences
In collaboration with Dr. Robert Synovec’s research team, LECO developed a region-based approach to identifying features in GCxGC contour plots. Raw MS data is segmented by “tiles” – small rectangles defined by retention times – and tiles with unique m/z fragments are identified in the samples. These tiles can then be rapidly compared with those in other samples, creating a heatmap of tile intensity that direct analysts’ attention to characteristic features of a sample or group of samples.
ChromaTOF Tile References
Enhanced GCxGC-TOFMS Biomarker Discovery
Fisher Ratio Analysis GCxGC–TOFMS using Null Distribution Approach
Receiver Operating Characteristic Curve Optimization within GCxGC-TOFMS
Improved Feature Selection Analysis in GCxGC-TOFMS
Chemical Characterization of Acid Alteration of Diesel Fuel
Tile-based Fisher Ratio Analysis using Yeast Metabolome Dataset
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